hypergas.hyper module

Hyper object to hold hyperspectral satellite data.

class hypergas.hyper.Hyper(filename=None, reader=None)

Bases: object

The Hyper Class.

Example usage:

from hypergas import Hyper

# create Hyper and open files
hyp = Hyper(filenames='/path/to/file/*')

# load datasets from input files
hyp.load()

# retrieve ch4
hyp.retrieve(self, wvl_intervals=[2110, 2450], species='ch4')

# orthorectification
hyp.terrain_corr(varname='rgb')

# export to NetCDF file
hyp.scene.save_datasets(datasets=['u10', 'v10', 'rgb', 'ch4'], filename='output.nc', writer='cf')

Initialize Hyper.

To load Hyper data, filename and reader must be specified:

hyp = Hyper(filenames=glob('/path/to/hyper/files/*'), reader='hsi_l1b')

Parameters:

• filename (list) – The files to be loaded.

• reader (str) – The name of the reader to use for loading the data.

denoise(varname='ch4', method='calibrated_tv_filter', weight=None)

Denoise the input data using hypergas.denoise.Denoise.

Parameters:

• varname (str) – The variable to be denoised.

• method (str) – The denoising method: “tv_filter” and “calibrated_tv_filter” (default).

• weight (int) – The weight for denoise_tv_chambolle. It would be neglected if method is “calibrated_tv_filter”. If weight is None (default) and method is “tv_filter”, the denoise_tv_chambolle will use the default value (0.1) which is too low for hyperspectral noisy gas field.

Returns:

da_denoise (DataArray) – Denoised data.

load(drop_waterbands=True)

Load data into xarray Dataset using Satpy.

Parameters:

drop_waterbands (bool) – whether to drop bands affected by water. Default: True.

plume_mask(varname='ch4_comb_denoise', n_min_threshold=5, sigma_threshold=1)

Create a priori plume masks using hypergas.a_priori_mask.Mask.

Parameters:

• scn (Satpy Scene) – Scene including one variable named segmentation which is calculated by hypergas.landmask.Land_mask(). segmentation (DataArray): 0: ocean, >0: land

• varname (str) – The variable used to create plume mask. (Recommend: <gas>_comb_denoise)

• n_min_threshold (int) – The minimum number of pixels per threshold for detecting features. (Default: 5).

• sigma_threshold (int) – Gaussian filter sigma for smoothing field. Default: 1. Because the <gas>_comb_denoise field is already smoothed, 1 should be high enough.

retrieve(wvl_intervals=None, species='ch4', algo='smf', mode='column', rad_dist='normal', land_mask=True, land_mask_source='OSM', cluster=False, plume_mask=None)

Retrieve trace gas enhancements.

Parameters:

• wvl_intervals (list) – The wavelength range (nm) used in matched filter. It can be one list or nested list. e.g. [2110, 2450] or [[1600, 1750], [2110, 2450]]. Deafult: [2110, 2450] for ch4 and [1930, 2200] for co2.

• species (str) – The species (“ch4”, “co2”) to be retrieved. Default: “ch4”.

• algo (str) – The matched filter algorithm, currently supporting only one algorithm: simple matched filter (smf). This is the original matched filter algorithm.

• mode (str) – The mode (“column” or “scene”) to apply matched filter. Default: “column”. Be careful of noise if you apply the matched filter for the whole scene.

• rad_dist (str) – The assumed rads distribution (“normal” or “lognormal”) Default: “normal”.

• land_mask (bool) – Whether apply the matched filter to continental and oceanic pixels seperately. Default: True.

• land_mask_source (str) – The data source of land mask (“OSM”, “GSHHS” or “Natural Earth”). Default: OSM.

• cluster (bool) – Whether apply the pixel classification. Default: False.

• plume_mask (numpy.ndarray) – 2D manual mask. 0: neglected pixels, 1: valid pixels. Default: None.

terrain_corr(varname='rgb', rpcs=None, gcps=None, gcp_crs=None)

Apply orthorectification using hypergas.orthorectification.Ortho.

Parameters:

• varname (str) – The variable to be orthorectified.

• rpcs – The Ground Control Points (gcps) or Rational Polynomial Coefficients (rpcs). If rpcs is None, we look for glt_x/glt_y data automatically.

Returns:

da_ortho (DataArray) – The orthorectified data.